Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation

27 June 2024

Yaxi Hu

Yian Ma

Papers citing "Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation"

27 / 27 papers shown

Title
Corrective Machine Unlearning Shashwat Goel Ameya Prabhu Philip Torr Ponnurangam Kumaraguru Amartya Sanyal OnRL 86 18 0 21 Feb 2024
Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features Cian Eastwood Shashank Singh Andrei Liviu Nicolicioiu Marin Vlastelica Julius von Kügelgen Bernhard Schölkopf OOD 93 20 0 19 Jul 2023
ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets Damien Teney Yong Lin Seong Joon Oh Ehsan Abbasnejad OOD 512 50 0 01 Sep 2022
A law of adversarial risk, interpolation, and label noise Daniel Paleka Amartya Sanyal NoLa AAML 61 10 0 08 Jul 2022
Verifying the Union of Manifolds Hypothesis for Image Data Bradley Brown Anthony L. Caterini Brendan Leigh Ross Jesse C. Cresswell Gabriel Loaiza-Ganem 94 43 0 06 Jul 2022
How Robust is Unsupervised Representation Learning to Distribution Shift? Yuge Shi Imant Daunhawer Julia E. Vogt Philip Torr Amartya Sanyal OOD 83 28 0 17 Jun 2022
Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution Ananya Kumar Aditi Raghunathan Robbie Jones Tengyu Ma Percy Liang OODD 124 683 0 21 Feb 2022
Deep Ensembles Work, But Are They Necessary? Taiga Abe E. Kelly Buchanan Geoff Pleiss R. Zemel John P. Cunningham OOD UQCV 116 65 0 14 Feb 2022
Covariate Shift in High-Dimensional Random Feature Regression Nilesh Tripuraneni Ben Adlam Jeffrey Pennington OOD 45 24 0 16 Nov 2021
Salient ImageNet: How to discover spurious features in Deep Learning? Sahil Singla Soheil Feizi AAML VLM 84 120 0 08 Oct 2021
Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization John Miller Rohan Taori Aditi Raghunathan Shiori Sagawa Pang Wei Koh Vaishaal Shankar Percy Liang Y. Carmon Ludwig Schmidt OODD OOD 80 278 0 09 Jul 2021
The Intrinsic Dimension of Images and Its Impact on Learning Phillip E. Pope Chen Zhu Ahmed Abdelkader Micah Goldblum Tom Goldstein 231 272 0 18 Apr 2021
WILDS: A Benchmark of in-the-Wild Distribution Shifts Pang Wei Koh Shiori Sagawa Henrik Marklund Sang Michael Xie Marvin Zhang ... A. Kundaje Emma Pierson Sergey Levine Chelsea Finn Percy Liang OOD 191 1,445 0 14 Dec 2020
Fair Classification with Group-Dependent Label Noise Jialu Wang Yang Liu Caleb C. Levy NoLa 44 103 0 31 Oct 2020
Learning explanations that are hard to vary Giambattista Parascandolo Alexander Neitz Antonio Orvieto Luigi Gresele Bernhard Schölkopf FAtt 63 185 0 01 Sep 2020
The Pitfalls of Simplicity Bias in Neural Networks Harshay Shah Kaustav Tamuly Aditi Raghunathan Prateek Jain Praneeth Netrapalli AAML 69 361 0 13 Jun 2020
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 608 4,893 0 23 Jan 2020
Deep Double Descent: Where Bigger Models and More Data Hurt Preetum Nakkiran Gal Kaplun Yamini Bansal Tristan Yang Boaz Barak Ilya Sutskever 121 945 0 04 Dec 2019
Confident Learning: Estimating Uncertainty in Dataset Labels Curtis G. Northcutt Lu Jiang Isaac L. Chuang NoLa 151 696 0 31 Oct 2019
Invariant Risk Minimization Martín Arjovsky Léon Bottou Ishaan Gulrajani David Lopez-Paz OOD 195 2,241 0 05 Jul 2019
Stable Rank Normalization for Improved Generalization in Neural Networks and GANs Amartya Sanyal Philip Torr P. Dokania 80 47 0 11 Jun 2019
Reconciling modern machine learning practice and the bias-variance trade-off M. Belkin Daniel J. Hsu Siyuan Ma Soumik Mandal 242 1,655 0 28 Dec 2018
Deep Learning Scaling is Predictable, Empirically Joel Hestness Sharan Narang Newsha Ardalani G. Diamos Heewoo Jun Hassan Kianinejad Md. Mostofa Ali Patwary Yang Yang Yanqi Zhou 107 742 0 01 Dec 2017
Functional Map of the World Gordon A. Christie Neil Fendley James Wilson R. Mukherjee VGen 75 398 0 21 Nov 2017
mixup: Beyond Empirical Risk Minimization Hongyi Zhang Moustapha Cissé Yann N. Dauphin David Lopez-Paz NoLa 282 9,797 0 25 Oct 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 310 12,117 0 19 Jun 2017
Understanding deep learning requires rethinking generalization Chiyuan Zhang Samy Bengio Moritz Hardt Benjamin Recht Oriol Vinyals HAI 345 4,636 0 10 Nov 2016